


import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
	import torch

if is_vision_available():
	from PIL import Image

	from transformers import ConditionalDetrImageProcessor




class       lowercase_					(unittest.TestCase   ):





				def __init__(				self       ,  lowercase_       ,  lowercase_=7       ,  lowercase_=3       ,  lowercase_=30       ,  lowercase_=400       ,  lowercase_=True       ,  lowercase_=None       ,  lowercase_=True       ,  lowercase_=[0.5, 0.5, 0.5]       ,  lowercase_=[0.5, 0.5, 0.5]       ,  lowercase_=True       ,  lowercase_=1 / 255       ,  lowercase_=True       ,  )  -> List[Any]:
											# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
											a__      =size if size is not None else {'shortest_edge': 18, 'longest_edge': 1333}
											a__      =parent
											a__      =batch_size
											a__      =num_channels
											a__      =min_resolution
											a__      =max_resolution
											a__      =do_resize
											a__      =size
											a__      =do_normalize
											a__      =image_mean
											a__      =image_std
											a__      =do_rescale
											a__      =rescale_factor
											a__      =do_pad





				def 						__UpperCamelCase    (				self)  -> Dict:
											return {
											    "do_resize": self.do_resize,
											    "size": self.size,
											    "do_normalize": self.do_normalize,
											    "image_mean": self.image_mean,
											    "image_std": self.image_std,
											    "do_rescale": self.do_rescale,
											    "rescale_factor": self.rescale_factor,
											    "do_pad": self.do_pad,
											}





				def 						__UpperCamelCase    (				self       ,  lowercase_       ,  lowercase_=False)  -> Any:
											if not batched:
																		a__      =image_inputs[0]
																		if isinstance(lowercase_       ,  Image.Image):
																									a__    ,     a__      =image.size
																		else:
																									a__    ,     a__      =image.shape[1], image.shape[2]
																		if w < h:
																									a__      =int(self.size['shortest_edge'] * h / w)
																									a__      =self.size['shortest_edge']
																		elif w > h:
																									a__      =self.size['shortest_edge']
																									a__      =int(self.size['shortest_edge'] * w / h)
																		else:
																									a__      =self.size['shortest_edge']
																									a__      =self.size['shortest_edge']

											else:
																		a__      =[]
																		for image in image_inputs:
																									a__    ,     a__      =self.get_expected_values([image])
																									expected_values.append((expected_height, expected_width))
																		a__      =max(lowercase_       ,  key=lambda lowercase_: item[0])[0]
																		a__      =max(lowercase_       ,  key=lambda lowercase_: item[1])[1]

											return expected_height, expected_width







@require_torch
@require_vision
class       lowercase_					(lowercase__      ,					unittest.TestCase   ):
				snake_case	    =ConditionalDetrImageProcessor if is_vision_available() else None





				def 						__UpperCamelCase    (				self)  -> Tuple:
											a__      =ConditionalDetrImageProcessingTester(self)





				@property
				def 						__UpperCamelCase    (				self)  -> Dict:
											return self.image_processor_tester.prepare_image_processor_dict()





				def 						__UpperCamelCase    (				self)  -> Any:
											a__      =self.image_processing_class(**self.image_processor_dict)
											self.assertTrue(hasattr(lowercase_       ,  'image_mean'))
											self.assertTrue(hasattr(lowercase_       ,  'image_std'))
											self.assertTrue(hasattr(lowercase_       ,  'do_normalize'))
											self.assertTrue(hasattr(lowercase_       ,  'do_resize'))
											self.assertTrue(hasattr(lowercase_       ,  'size'))





				def 						__UpperCamelCase    (				self)  -> Union[str, Any]:
											a__      =self.image_processing_class.from_dict(self.image_processor_dict)
											self.assertEqual(image_processor.size       ,  {'shortest_edge': 18, 'longest_edge': 1333})
											self.assertEqual(image_processor.do_pad       ,  lowercase_)

											a__      =self.image_processing_class.from_dict(
											    self.image_processor_dict       ,  size=42       ,  max_size=84       ,  pad_and_return_pixel_mask=lowercase_)
											self.assertEqual(image_processor.size       ,  {'shortest_edge': 42, 'longest_edge': 84})
											self.assertEqual(image_processor.do_pad       ,  lowercase_)





				def 						__UpperCamelCase    (				self)  -> Optional[Any]:
											pass





				def 						__UpperCamelCase    (				self)  -> Optional[Any]:
											# Initialize image_processing
											a__      =self.image_processing_class(**self.image_processor_dict)
											# create random PIL images
											a__      =prepare_image_inputs(self.image_processor_tester       ,  equal_resolution=lowercase_)
											for image in image_inputs:
																		self.assertIsInstance(lowercase_       ,  Image.Image)

											# Test not batched input
											a__      =image_processing(image_inputs[0]       ,  return_tensors='pt').pixel_values

											a__    ,     a__      =self.image_processor_tester.get_expected_values(lowercase_)

											self.assertEqual(
											    encoded_images.shape       ,  (1, self.image_processor_tester.num_channels, expected_height, expected_width)       ,  )

											# Test batched
											a__    ,     a__      =self.image_processor_tester.get_expected_values(lowercase_       ,  batched=lowercase_)

											a__      =image_processing(lowercase_       ,  return_tensors='pt').pixel_values
											self.assertEqual(
											    encoded_images.shape       ,  (
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        expected_height,
											        expected_width,
											    )       ,  )





				def 						__UpperCamelCase    (				self)  -> Optional[int]:
											# Initialize image_processing
											a__      =self.image_processing_class(**self.image_processor_dict)
											# create random numpy tensors
											a__      =prepare_image_inputs(self.image_processor_tester       ,  equal_resolution=lowercase_       ,  numpify=lowercase_)
											for image in image_inputs:
																		self.assertIsInstance(lowercase_       ,  np.ndarray)

											# Test not batched input
											a__      =image_processing(image_inputs[0]       ,  return_tensors='pt').pixel_values

											a__    ,     a__      =self.image_processor_tester.get_expected_values(lowercase_)

											self.assertEqual(
											    encoded_images.shape       ,  (1, self.image_processor_tester.num_channels, expected_height, expected_width)       ,  )

											# Test batched
											a__      =image_processing(lowercase_       ,  return_tensors='pt').pixel_values

											a__    ,     a__      =self.image_processor_tester.get_expected_values(lowercase_       ,  batched=lowercase_)

											self.assertEqual(
											    encoded_images.shape       ,  (
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        expected_height,
											        expected_width,
											    )       ,  )





				def 						__UpperCamelCase    (				self)  -> Optional[Any]:
											# Initialize image_processing
											a__      =self.image_processing_class(**self.image_processor_dict)
											# create random PyTorch tensors
											a__      =prepare_image_inputs(self.image_processor_tester       ,  equal_resolution=lowercase_       ,  torchify=lowercase_)
											for image in image_inputs:
																		self.assertIsInstance(lowercase_       ,  torch.Tensor)

											# Test not batched input
											a__      =image_processing(image_inputs[0]       ,  return_tensors='pt').pixel_values

											a__    ,     a__      =self.image_processor_tester.get_expected_values(lowercase_)

											self.assertEqual(
											    encoded_images.shape       ,  (1, self.image_processor_tester.num_channels, expected_height, expected_width)       ,  )

											# Test batched
											a__      =image_processing(lowercase_       ,  return_tensors='pt').pixel_values

											a__    ,     a__      =self.image_processor_tester.get_expected_values(lowercase_       ,  batched=lowercase_)

											self.assertEqual(
											    encoded_images.shape       ,  (
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        expected_height,
											        expected_width,
											    )       ,  )





				@slow
				def 						__UpperCamelCase    (				self)  -> List[Any]:
											# prepare image and target
											a__      =Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png')
											with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt'       ,  'r') as f:
																		a__      =json.loads(f.read())

											a__      ={'image_id': 39769, 'annotations': target}

											# encode them
											a__      =ConditionalDetrImageProcessor.from_pretrained('microsoft/conditional-detr-resnet-50')
											a__      =image_processing(images=lowercase_       ,  annotations=lowercase_       ,  return_tensors='pt')

											# verify pixel values
											a__      =torch.Size([1, 3, 800, 1066])
											self.assertEqual(encoding['pixel_values'].shape       ,  lowercase_)

											a__      =torch.tensor([0.27_96, 0.31_38, 0.34_81])
											self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]       ,  lowercase_       ,  atol=1e-4))

											# verify area
											a__      =torch.tensor([58_87.96_00, 1_12_50.20_61, 48_93_53.84_38, 83_71_22.75_00, 14_79_67.51_56, 16_57_32.34_38])
											self.assertTrue(torch.allclose(encoding['labels'][0]['area']       ,  lowercase_))
											# verify boxes
											a__      =torch.Size([6, 4])
											self.assertEqual(encoding['labels'][0]['boxes'].shape       ,  lowercase_)
											a__      =torch.tensor([0.55_03, 0.27_65, 0.06_04, 0.22_15])
											self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]       ,  lowercase_       ,  atol=1e-3))
											# verify image_id
											a__      =torch.tensor([39769])
											self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']       ,  lowercase_))
											# verify is_crowd
											a__      =torch.tensor([0, 0, 0, 0, 0, 0])
											self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']       ,  lowercase_))
											# verify class_labels
											a__      =torch.tensor([75, 75, 63, 65, 17, 17])
											self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']       ,  lowercase_))
											# verify orig_size
											a__      =torch.tensor([480, 640])
											self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']       ,  lowercase_))
											# verify size
											a__      =torch.tensor([800, 1066])
											self.assertTrue(torch.allclose(encoding['labels'][0]['size']       ,  lowercase_))





				@slow
				def 						__UpperCamelCase    (				self)  -> Dict:
											# prepare image, target and masks_path
											a__      =Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png')
											with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'       ,  'r') as f:
																		a__      =json.loads(f.read())

											a__      ={'file_name': '000000039769.png', 'image_id': 39769, 'segments_info': target}

											a__      =pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic')

											# encode them
											a__      =ConditionalDetrImageProcessor(format='coco_panoptic')
											a__      =image_processing(images=lowercase_       ,  annotations=lowercase_       ,  masks_path=lowercase_       ,  return_tensors='pt')

											# verify pixel values
											a__      =torch.Size([1, 3, 800, 1066])
											self.assertEqual(encoding['pixel_values'].shape       ,  lowercase_)

											a__      =torch.tensor([0.27_96, 0.31_38, 0.34_81])
											self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]       ,  lowercase_       ,  atol=1e-4))

											# verify area
											a__      =torch.tensor([14_79_79.68_75, 16_55_27.04_69, 48_46_38.59_38, 1_12_92.93_75, 58_79.65_62, 76_34.11_47])
											self.assertTrue(torch.allclose(encoding['labels'][0]['area']       ,  lowercase_))
											# verify boxes
											a__      =torch.Size([6, 4])
											self.assertEqual(encoding['labels'][0]['boxes'].shape       ,  lowercase_)
											a__      =torch.tensor([0.26_25, 0.54_37, 0.46_88, 0.86_25])
											self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]       ,  lowercase_       ,  atol=1e-3))
											# verify image_id
											a__      =torch.tensor([39769])
											self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']       ,  lowercase_))
											# verify is_crowd
											a__      =torch.tensor([0, 0, 0, 0, 0, 0])
											self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']       ,  lowercase_))
											# verify class_labels
											a__      =torch.tensor([17, 17, 63, 75, 75, 93])
											self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']       ,  lowercase_))
											# verify masks
											a__      =822873
											self.assertEqual(encoding['labels'][0]['masks'].sum().item()       ,  lowercase_)
											# verify orig_size
											a__      =torch.tensor([480, 640])
											self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']       ,  lowercase_))
											# verify size
											a__      =torch.tensor([800, 1066])
											self.assertTrue(torch.allclose(encoding['labels'][0]['size']       ,  lowercase_))



"""simple docstring"""


import logging
import os
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from tempfile import TemporaryDirectory
from typing import List, Optional

import faiss
import torch
from datasets import Features, Sequence, Value, load_dataset

from transformers import DPRContextEncoder, DPRContextEncoderTokenizerFast, HfArgumentParser


SCREAMING_SNAKE_CASE			  =  logging.getLogger(__name__)
torch.set_grad_enabled(False)
SCREAMING_SNAKE_CASE			  =  """cuda""" if torch.cuda.is_available() else """cpu"""



def 						lowerCamelCase__      (				UpperCAmelCase_		,   UpperCAmelCase_=1_00		,   UpperCAmelCase_=" "					)->				List[str]:







       """simple docstring"""
       UpperCamelCase       			=		text.split(UpperCAmelCase_					)
       return [character.join(text[i : i + n]					).strip() for i in range(0		,   len(UpperCAmelCase_					)		,   UpperCAmelCase_					)]



def 						lowerCamelCase__      (				UpperCAmelCase_					)->				dict:







       """simple docstring"""
       UpperCamelCase			,							UpperCamelCase       			=		[], []
       for title, text in zip(documents["title"]		,   documents["text"]					):
              if text is not None:
                     for passage in split_text(UpperCAmelCase_					):
                            titles.append(title if title is not None else ""					)
                            texts.append(UpperCAmelCase_					)
       return {"title": titles, "text": texts}



def 						lowerCamelCase__      (				UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_					)->				dict:







       """simple docstring"""
       UpperCamelCase       			=		ctx_tokenizer(
           documents["title"]		,   documents["text"]		,   truncation=UpperCAmelCase_		,   padding="longest"		,   return_tensors="pt"					)["input_ids"]
       UpperCamelCase       			=		ctx_encoder(input_ids.to(device=UpperCAmelCase_					)		,   return_dict=UpperCAmelCase_					).pooler_output
       return {"embeddings": embeddings.detach().cpu().numpy()}



def 						lowerCamelCase__      (				UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_		,   )->				List[str]:







       """simple docstring"""
       ######################################
       logger.info("Step 1 - Create the dataset"					)
       ######################################

       # The dataset needed for RAG must have three columns:
       # - title (string): title of the document
       # - text (string): text of a passage of the document
       # - embeddings (array of dimension d): DPR representation of the passage
       # Let's say you have documents in tab-separated csv files with columns "title" and "text"
       assert os.path.isfile(rag_example_args.csv_path					), "Please provide a valid path to a csv file"

       # You can load a Dataset object this way
       UpperCamelCase       			=		load_dataset(
           "csv"		,   data_files=[rag_example_args.csv_path]		,   split="train"		,   delimiter="\t"		,   column_names=["title", "text"]					)

       # More info about loading csv files in the documentation: https://huggingface.co/docs/datasets/loading_datasets.html?highlight=csv#csv-files

       # Then split the documents into passages of 100 words
       UpperCamelCase       			=		dataset.map(UpperCAmelCase_		,   batched=UpperCAmelCase_		,   num_proc=processing_args.num_proc					)

       # And compute the embeddings
       UpperCamelCase       			=		DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name					).to(device=UpperCAmelCase_					)
       UpperCamelCase       			=		DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name					)
       UpperCamelCase       			=		Features(
           {"text": Value("string"					), "title": Value("string"					), "embeddings": Sequence(Value("float32"					)					)}					)  # optional, save as float32 instead of float64 to save space
       UpperCamelCase       			=		dataset.map(
           partial(UpperCAmelCase_		,   ctx_encoder=UpperCAmelCase_		,   ctx_tokenizer=UpperCAmelCase_					)		,   batched=UpperCAmelCase_		,   batch_size=processing_args.batch_size		,   features=UpperCAmelCase_		,   )

       # And finally save your dataset
       UpperCamelCase       			=		os.path.join(rag_example_args.output_dir		,   "my_knowledge_dataset"					)
       dataset.save_to_disk(UpperCAmelCase_					)
       # from datasets import load_from_disk
       # dataset = load_from_disk(passages_path)  # to reload the dataset

       ######################################
       logger.info("Step 2 - Index the dataset"					)
       ######################################

       # Let's use the Faiss implementation of HNSW for fast approximate nearest neighbor search
       UpperCamelCase       			=		faiss.IndexHNSWFlat(index_hnsw_args.d		,   index_hnsw_args.m		,   faiss.METRIC_INNER_PRODUCT					)
       dataset.add_faiss_index("embeddings"		,   custom_index=UpperCAmelCase_					)

       # And save the index
       UpperCamelCase       			=		os.path.join(rag_example_args.output_dir		,   "my_knowledge_dataset_hnsw_index.faiss"					)
       dataset.get_index("embeddings"					).save(UpperCAmelCase_					)
       # dataset.load_faiss_index("embeddings", index_path)  # to reload the index



@dataclass
class 	__a	:
       UpperCamelCase_		:       str				=      field(
           default=str(Path(_lowerCAmelCase						).parent / '''test_run''' / '''dummy-kb''' / '''my_knowledge_dataset.csv'''						)     ,  metadata={'''help''': '''Path to a tab-separated csv file with columns \'title\' and \'text\''''}     ,  )
       UpperCamelCase_		:       Optional[str]				=      field(
           default=_lowerCAmelCase     ,  metadata={'''help''': '''Question that is passed as input to RAG. Default is \'What does Moses\' rod turn into ?\'.'''}     ,  )
       UpperCamelCase_		:       str				=      field(
           default='''facebook/rag-sequence-nq'''     ,  metadata={'''help''': '''The RAG model to use. Either \'facebook/rag-sequence-nq\' or \'facebook/rag-token-nq\''''}     ,  )
       UpperCamelCase_		:       str				=      field(
           default='''facebook/dpr-ctx_encoder-multiset-base'''     ,  metadata={
               '''help''': (
                   '''The DPR context encoder model to use. Either \'facebook/dpr-ctx_encoder-single-nq-base\' or'''
                   ''' \'facebook/dpr-ctx_encoder-multiset-base\''''
               )
           }     ,  )
       UpperCamelCase_		:       Optional[str]				=      field(
           default=str(Path(_lowerCAmelCase						).parent / '''test_run''' / '''dummy-kb'''						)     ,  metadata={'''help''': '''Path to a directory where the dataset passages and the index will be saved'''}     ,  )





@dataclass
class 	__a	:
       UpperCamelCase_		:       Optional[int]				=      field(
           default=_lowerCAmelCase     ,  metadata={
               '''help''': '''The number of processes to use to split the documents into passages. Default is single process.'''
           }     ,  )
       UpperCamelCase_		:       int				=      field(
           default=16     ,  metadata={
               '''help''': '''The batch size to use when computing the passages embeddings using the DPR context encoder.'''
           }     ,  )





@dataclass
class 	__a	:
       UpperCamelCase_		:       int				=      field(
           default=768     ,  metadata={'''help''': '''The dimension of the embeddings to pass to the HNSW Faiss index.'''}     ,  )
       UpperCamelCase_		:       int				=      field(
           default=128     ,  metadata={
               '''help''': (
                   '''The number of bi-directional links created for every new element during the HNSW index construction.'''
               )
           }     ,  )


if __name__ == "__main__":
      logging.basicConfig(level=logging.WARNING)
      logger.setLevel(logging.INFO)

      SCREAMING_SNAKE_CASE			  =  HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
      SCREAMING_SNAKE_CASE  ,   SCREAMING_SNAKE_CASE  ,   SCREAMING_SNAKE_CASE			  =  parser.parse_args_into_dataclasses()
      with TemporaryDirectory() as tmp_dir:
            SCREAMING_SNAKE_CASE			  =  rag_example_args.output_dir or tmp_dir
            main(rag_example_args, processing_args, index_hnsw_args)





